[jira] [Created] (ARROW-1660) pandas field values are messed up across rows

["MIkhail Osckin (JIRA)" <ji...@apache.org>]

MIkhail Osckin created ARROW-1660:
-------------------------------------

             Summary: pandas field values are messed up across rows
                 Key: ARROW-1660
                 URL: https://issues.apache.org/jira/browse/ARROW-1660
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
    Affects Versions: 0.7.1
         Environment: 4.4.0-72-generic #93-Ubuntu SMP x86_64, python3
            Reporter: MIkhail Osckin


I have the following scala case class to store sparse matrix data to read it later using python

case class CooVector(
    id: Int,
    row_ids: Seq[Int],
    rowsIdx: Seq[Int],
    colIdx: Seq[Int],
    data: Seq[Double])

I save the dataset of this type to multiple parquet files using spark and then read it using pyarrow.parquet and convert the result to pandas dataset.

The problem i have is that some values end up in wrong rows, for example, row_ids might end up in wrong cooVector row. I have no idea what the reason is but might be it is related to the fact that the fields are of variable sizes.




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